Introduction to GIS and mapping in R {https://t.co/yupf5KT4My} #rstats #DataScience
— R-bloggers (@Rbloggers) July 15, 2022
How to write a function in R and apply it to a data frame using map functions from {purr} {https://t.co/0L3YldkDEL} #rstats #DataScience
— R-bloggers (@Rbloggers) July 13, 2022
Eight R Tidyverse tips for everyday data engineering {https://t.co/vpMtqNCM06} #rstats #DataScience
— R-bloggers (@Rbloggers) July 14, 2022
Network Graphs in R {https://t.co/bgKJ31WW1d} #rstats #DataScience
— R-bloggers (@Rbloggers) July 13, 2022
Input R Shiny: Shiny Input Examples with shiny.fluent {https://t.co/dUbwYjfSbU} #rstats #DataScience
— R-bloggers (@Rbloggers) July 11, 2022
Random Forest Machine Learning Introduction {https://t.co/uDgFhwVLxL} #rstats #DataScience
— R-bloggers (@Rbloggers) July 12, 2022
Convert Multiple Columns to Numeric in R {https://t.co/bKBQn7VeRM} #rstats #DataScience
— R-bloggers (@Rbloggers) July 9, 2022
How to Calculate Lag by Group in R? {https://t.co/kdkApcEPP8} #rstats #DataScience
— R-bloggers (@Rbloggers) July 9, 2022
How to Use Mutate function in R {https://t.co/m3Jw76MpjF} #rstats #DataScience
— R-bloggers (@Rbloggers) July 11, 2022
Recreating the Shiny App tutorial with a Plumber API + React: Part 2 {https://t.co/SZM9pKeKrJ} #rstats #DataScience
— R-bloggers (@Rbloggers) July 14, 2022
Combine project management and data analysis using Tiddlywiki and RMarkdown {https://t.co/zmX20KxWoS} #rstats #DataScience
— R-bloggers (@Rbloggers) July 11, 2022
Introductory R/exams Course by Andrew Zammit-Mangion {https://t.co/k4cABOj51z} #rstats #DataScience
— R-bloggers (@Rbloggers) July 11, 2022
Packages for Exploratory Data Analysis in R {https://t.co/wXDlQF2uXx} #rstats #DataScience
— R-bloggers (@Rbloggers) July 8, 2022
An introductory course in Shiny {https://t.co/MWxFJMO4uY} #rstats #DataScience
— R-bloggers (@Rbloggers) June 26, 2022
Introduction to GIS and mapping in R {https://t.co/yupf5KT4My} #rstats #DataScience
— R-bloggers (@Rbloggers) July 15, 2022
How to write a function in R and apply it to a data frame using map functions from {purr} {https://t.co/0L3YldkDEL} #rstats #DataScience
— R-bloggers (@Rbloggers) July 13, 2022
Reshaping data frames using pivot functions from {tidyr} and tally from {dplyr} {https://t.co/BKWSBzgIlI} #rstats #DataScience
— R-bloggers (@Rbloggers) July 6, 2022
What To Do (And Not to Do) with Modeling Proportions/Fractional Outcomes {https://t.co/d8rTDTIAkA} #rstats #DataScience
— R-bloggers (@Rbloggers) June 27, 2022
Webscraping in R with Rvest {https://t.co/VuzCHurZQ7} #rstats #DataScience
— R-bloggers (@Rbloggers) June 25, 2022
The Poisson distribution: From basic probability theory to regression models {https://t.co/lxZvYTrLtB} #rstats #DataScience
— R-bloggers (@Rbloggers) June 23, 2022
How to Group and Summarize Data in R {https://t.co/6PPqmthbJL} #rstats #DataScience
— R-bloggers (@Rbloggers) June 26, 2022
Eight R Tidyverse tips for everyday data engineering {https://t.co/vpMtqNCM06} #rstats #DataScience
— R-bloggers (@Rbloggers) July 14, 2022
Network Graphs in R {https://t.co/bgKJ31WW1d} #rstats #DataScience
— R-bloggers (@Rbloggers) July 13, 2022
Shiny and Arrow {https://t.co/9jVwlA2mgC} #rstats #DataScience
— R-bloggers (@Rbloggers) June 27, 2022
---
title: "RBloggers Top Tweets"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
source_code: embed
theme:
version: 4
bootswatch: yeti
css: styles/main.css
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(httr)
library(lubridate)
library(jsonlite)
library(purrr)
rbloggers <- fromJSON("data/rbloggers.json")
get_tweet_embed <- function(user, status_id) {
url <-
stringr::str_glue(
"https://publish.twitter.com/oembed?url=https://twitter.com/{user}/status/{status_id}&partner=&hide_thread=false"
)
response <- GET(url) %>%
content()
return(shiny::HTML(response$html))
}
```
Column {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Top Tweets - 7 days {.tweet-wall}
```{r}
rblog_7 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 7, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_7_html <-
map2_chr(rblog_7$screen_name, rblog_7$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_7_html}"))
```
### Top Tweets - 30 days {.tweet-wall}
```{r}
rblog_30 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 30, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_30_html <-
map2_chr(rblog_30$screen_name, rblog_30$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_30_html}"))
```